Marriage and Statistics through Space and Time

Project Summary

Over ten weeks, Mathematics/Economics majors Khuong (Lucas) Do and Jason Law joined forces with Analytical Political Economy Masters student Feixiao Chen to analyze the spati-temporal distribution of birth addresses in North Carolina. The goal of the project was to understand how/whether the distributions of different demographic categories (white/black, married/unmarried, etc.) differed, and how these differences connected to a variety of socioeconomic indicators.

Themes and Categories
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Project Results: Working with over 2 million births from the North Carolina Birth Records Database, as well as block-group level socioeconomic indicators from the Census and American Community Survey, the team used spatial-statistics techniques to find 'hotspots' corresponding to clusters of births in specific categories. They then showed significant connections between socioeconomic indicators and cluster membership. Finally, they employed cutting-edge machine-learning techniques to quantify the distributional differences between births in different demographic categories.

Partially funded by the Sanford School of Public Policy

Click here for the Executive Summary

Faculty Leads: Christina Gibson-Davis, Paul Bendich

Project Manager: Lizzy Huang

Related People

Related Projects

Marine mammals exhibit extreme physiological and behavioral adaptions that allow them to dive hundreds to thousands of meters underwater despite their need to breathe air at the surface. Through the development of new remote monitoring technologies, we are just beginning to understand the mechanisms by which they are able to execute these extreme behaviors. Long- term animal-borne tags can now record location, dive depth, and dive duration and then transmit these data to satellite receivers, enabling remote access to behavior occurring both many kilometers out to sea and several kilometers below the ocean surface. 

The aim of this Data Expedition was for students to learn hands-on data visualization techniques using a variety of data types. Students first discussed how data visualization is useful, and tips to make graphs both visually appealing and easy to understand. 

The aim of our data expeditions course was to give students in Bio 190S-0.2, a summer session course in sensory systems, an introduction to how real data may actually look and how they may actually be analyzed. Over the course of a two-hour class session, 16 students ranging from 16-22 years old were given the opportunity to explore a dataset on the color vision capabilities of three species of cleaner shrimp.